2009 22nd IEEE International Symposium on Computer-Based Medical Systems (2009)
Albuquerque, NM, USA
Aug. 2, 2009 to Aug. 5, 2009
Rahul Singh , Department of Computer Science, San Francisco State University, San Francisco CA 94132
Michalis Pittas , Department of Computer Science, San Francisco State University, San Francisco CA 94132
Ido Heskia , Department of Mathematics, San Francisco State University, San Francisco CA 94132
Fengyun Xu , Sandler Center for Basic Research in Parasitic Diseases University of California, San Francisco, CA 94158
James McKerrow , Sandler Center for Basic Research in Parasitic Diseases University of California, San Francisco, CA 94158
At the state-of-the-art in drug discovery, one of the key challenges is to develop high-throughput screening (HTS) techniques that can measure changes as a continuum of complex phenotypes induced in a target pathogen. Such measurements are crucial in developing therapeutics against diseases like schistosomiasis, trypanosomiasis, and leishmaniasis, which impact millions worldwide. These diseases are caused by parasites that can manifest a variety of phenotypes at any given point in time in response to drugs. Consequently, a single end-point measurement of ‘live or death’ (e.g., ED<inf>50</inf> value) commonly used for lead identification is over-simplistic. In our method to address this problem, the parasites are tracked during the entire course of (video) recorded observations and changes in their appearance-based and behavioral characteristics quantified using geometric, texture-based, color-based, and motion-based descriptors. Subsequently, within the on-line setting, machine learning techniques are used classify the exhibited phenotypes into well defined categories. Important advancements introduced as a consequence of the proposed approach include: (1) ability to assess the interactions between putative drugs and parasites in terms of multiple appearance and behavior-based phenotypes, (2) automatic classification and quantification of pathogen phenotypes. Experimental data from lead identification studies against the disease Schistosomiasis validate the proposed methodology.
J. McKerrow, C. R. Caffrey, M. Pittas, F. Xu, R. Singh and I. Heskia, "Automated image-based phenotypic screening for high-throughput drug discovery," 2009 22nd IEEE International Symposium on Computer-Based Medical Systems(CBMS), Albuquerque, NM, USA, 2009, pp. 1-8.